Near Real-Time Disturbance Detection in Terrestrial Ecosystems Using Satellite Image Time Series: Drought Detection in Somalia

Near real-time monitoring of ecosystem disturbances is critical for addressing impacts on carbon dynamics, biodiversity, and socio-ecological processes. Satellite remote sensing enables cost-effective and accurate monitoring at frequent time steps over large areas. Yet, generic methods to detect disturbances within newly captured satellite images are lacking. We propose a generic time series based disturbance detection approach by modelling stable historical behaviour to enable detection of abnormal changes within newly acquired data. Time series of vegetation greenness provide a measure for terrestrial vegetation productivity over the last decades covering the whole world and contain essential information related land cover dynamics and disturbances. Here, we assess and demonstrate the method by (1) simulating time series of vegetation greenness data from satellite data with different amount of noise, seasonality and disturbances representing a wide range of terrestrial ecosystems, (2) applying it to real satellite greenness image time series between February 2000 and July 2011 covering Somalia to detect drought related vegetation disturbances. First, simulation results illustrate that disturbances are successfully detected in near real-time while being robust for seasonality and noise. Second, major drought related disturbance corresponding with most drought stressed regions in Somalia are detected from mid 2010 onwards and confirm proof-of-concept of the method. The method can be integrated within current operational early warning systems and has the potential to detect a wide variety of disturbances (e.g. deforestation, flood damage, etc.). It can analyse in-situ or satellite data time series of biophysical indicators from local to global scale since it is fast, does not depend on thresholds or definitions and does not require time series gap filling.early warning, real-time monitoring, global change, disturbance, time series, remote sensing, vegetation and climate dynamics

Massively-Parallel Break Detection for Satellite Data

The field of remote sensing is nowadays faced with huge amounts of data. While this offers a variety of exciting research opportunities, it also yields significant challenges regarding both computation time and space requirements. In practice, the sheer data volumes render existing approaches too slow for processing and analyzing all the available data. This work aims at accelerating BFAST, one of the state-of-the-art methods for break detection given satellite image time series. In particular, we propose a massively-parallel implementation for BFAST that can effectively make use of modern parallel compute devices such as GPUs. Our experimental evaluation shows that the proposed GPU implementation is up to four orders of magnitude faster than the existing publicly available implementation and up to ten times faster than a corresponding multi-threaded CPU execution. The dramatic decrease in running time renders the analysis of significantly larger datasets possible in seconds or minutes instead of hours or days. We demonstrate the practical benefits of our implementations given both artificial and real datasets.Comment: 10 page

Near Real-Time Disturbance Detection in Terrestrial Ecosystems Using Satellite Image Time Series: Drought Detection in Somalia

The article promotes an innovative method of monitoring of ecosystems that uses satellite data to detect drought.Maqaalku wuxuu tala ahaan soo jeedinayaa hab cusub oo lagu kormeero hab-wadanoolaashaha, iyagoo la adeegsanayo xitaa sawirro dayaxgacmeed si loo sahmiyo abaaraha.L'articolo propone un metodo innovativo di monitoraggio degli ecosistemi che utilizza anche immagini satellitari per il rilevamento delle siccità

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series:Does Forest Type Matter?

Tropical environments present a unique challenge for optical time series analysis, primarily owing to fragmented data availability, persistent cloud cover and atmospheric aerosols. Additionally, little is known of whether the performance of time series change detection is affected by diverse forest types found in tropical dry regions. In this paper, we develop a methodology for mapping forest clearing in Southeast Asia using a study region characterised by heterogeneous forest types. Moderate Resolution Imaging Spectroradiometer (MODIS) time series are decomposed using Breaks For Additive Season and Trend (BFAST) and breakpoints, trend, and seasonal components are combined in a binomial probability model to distinguish between cleared and stable forest. We found that the addition of seasonality and trend information improves the change model performance compared to using breakpoints alone. We also demonstrate the value of considering forest type in disturbance mapping in comparison to the more common approach that combines all forest types into a single generalised forest class. By taking a generalised forest approach, there is less control over the error distribution in each forest type. Dry-deciduous and evergreen forests are especially sensitive to error imbalances using a generalised forest model i.e., clearances were underestimated in evergreen forest, and overestimated in dry-deciduous forest. This suggests that forest type needs to be considered in time series change mapping, especially in heterogeneous forest regions. Our approach builds towards improving large-area monitoring of forest-diverse regions such as Southeast Asia. The findings of this study should also be transferable across optical sensors and are therefore relevant for the future availability of dense time series for the tropics at higher spatial resolutions

Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia

High spatio-temporal resolution optical remote sensing data provide unprecedented opportunities to monitor and detect forest disturbance and loss. To demonstrate this potential, a 12-year time series (2000 to 2011) with an 8-day interval of a 30m spatial resolution data was generated by the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) with Landsat sensor observations and Moderate Resolution Imaging Spectroradiometer (MODIS) data as input. The time series showed a close relationship over homogeneous forested and grassland sites, with r values of 0.99 between Landsat and the closest STARFM simulated data; and values of 0.84 and 0.94 between MODIS and STARFM. The time and magnitude of clearing and re-clearing events were estimated through a phenological breakpoint analysis, with 96.2% of the estimated breakpoints of the clearing event and 83.6% of the re-clearing event being within 40days of the true clearing. The study highlights the benefits of using these moderate resolution data for quantifying and understanding land cover change in open forest environments

Detecting clear-cuts and decreases in forest vitality using MODIS NDVI time series

This paper examines the potential of MODIS-NDVI time series for detecting clear-cuts in a coniferous forest stand in the south of France. The proposed approach forms part of a survey monitoring the status of forest health and evaluating the forest decline phenomena observed over the last few decades. One of the prerequisites for this survey was that a rapid and easily reproducible method had to be developed that differentiates between forest clear-cuts and changes in forest health induced by environmental factors such as summer droughts. The proposed approach is based on analysis of the breakpoints detected within NDVI time series, using the "Break for Additive Seasonal and Trend" (BFAST) algorithm. To overcome difficulties detecting small areas on the study site, we chose a probabilistic approach based on the use of a conditional inference tree. For model calibration, clear-cut reference data were produced at MODIS resolution (250 m). According to the magnitude of the detected breakpoints, probability classes for the presence of clear-cuts were defined, from greater than 90% to less than 3% probability of a clear-cut. One of the advantages of the probabilistic model is that it allows end users to choose an acceptable level of uncertainty depending on the application. In addition, the use of BFAST allows events to be dated, thus making it possible to perform a retrospective analysis of decreases in forest vitality in the study area

Characterizing Tropical Forest Cover Loss Using Dense Sentinel-1 Data and Active Fire Alerts

Fire use for land management is widespread in natural tropical and plantation forests, causing major environmental and economic damage. Recent studies combining active fire alerts with annual forest-cover loss information identified fire-related forest-cover loss areas well, but do not provide detailed understanding on how fires and forest-cover loss are temporally related. Here, we combine Sentinel-1-based, near real-time forest cover information with Visible Infrared Imaging Radiometer Suite (VIIRS) active fire alerts, and for the first time, characterize the temporal relationship between fires and tropical forest-cover loss at high temporal detail and medium spatial scale. We quantify fire-related forest-cover loss and separate fires that predate, coincide with, and postdate forest-cover loss. For the Province of Riau, Indonesia, dense Sentinel-1 C-band Synthetic Aperture Radar data with guaranteed observations of at least every 12 days allowed for confident and timely forest-cover-loss detection in natural and plantation forest with user’s and producer’s accuracy above 95%. Forest-cover loss was detected and confirmed within 22 days in natural forest and within 15 days in plantation forest. This difference can primarily be related to different change processes and dynamics in natural and plantation forest. For the period between 1 January 2016 and 30 June 2017, fire-related forest-cover loss accounted for about one third of the natural forest-cover loss, while in plantation forest, less than ten percent of the forest-cover loss was fire-related. We found clear spatial patterns of fires predating, coinciding with, or postdating forest-cover loss. Only the minority of fires in natural and plantation forest temporally coincided with forest-cover loss (13% and 16%) and can thus be confidently attributed as direct cause of forest-cover loss. The majority of the fires predated (64% and 58%) or postdated forest-cover loss (23% and 26%), and should be attributed to other key land management practices. Detailed and timely information on how fires and forest cover loss are temporally related can support tropical forest management, policy development, and law enforcement to reduce unsustainable and illegal fire use in the tropics

Global-scale characterization of turning points in arid and semi-arid ecosystem functioning

Aim: Changes in dryland ecosystem functioning are threatening the well‐being of human populations worldwide, and land degradation, exacerbated by climate change, contributes to biodiversity loss and puts pressures on sustainable livelihoods. Here, abrupt changes in ecosystem functioning [so‐called turning points (TPs)] were detected using time series of Earth observation data. Hotspot areas of high TP occurrence were identified, observed changes characterized and insights gained on potential drivers for these changes.Location: Arid and semi‐arid regions.Time period: 1982–2015.Methods: We used a time series segmentation technique (breaks for additive season and trend) to detect breakpoints in rain‐use efficiency as a means of analysing changes in ecosystem functioning. A new typology to characterize the detected changes was proposed and evaluated, at regional to local scales, for a set of case studies. Ancillary data on population and drought were used to provide insights on potential drivers of TP occurrence.Results: Turning points in ecosystem functioning were found in 13.6% (c. 2.1 × 106 km2) of global drylands. Turning point hotspots were primarily observed in North America, the Sahel, Central Asia and Australia. In North America, the majority of TPs (62.6%) were characterized by a decreasing trend in ecosystem functioning, whereas for the other regions, a positive reversal in ecosystem functioning was prevalent. Further analysis showed that: (a) both climatic and anthropogenic pressure influenced the occurrence of TPs in North America; (b) Sahelian grasslands were primarily characterized by drought‐induced TPs; and (c) high anthropogenic pressure coincided with the occurrence of TPs in Asia and Australia.Main conclusions: By developing a new typology targeting the categorization of abrupt and gradual changes in ecosystem functioning, we detected and characterized TPs in global drylands. This TP characterization is a first crucial step towards understanding the drivers of change and supporting better decision‐making for ecosystem conservation and management in drylands

The Impact of Caesarean Delivery on Paracetamol and Ketorolac Pharmacokinetics: A Paired Analysis

Pharmacokinetics is a first, but essential step to improve population-tailored postoperative analgesia, also after Caesarean delivery. We therefore aimed to quantify the impact of caesarean delivery on the pharmacokinetics of intravenous (iv) paracetamol (2 g, single dose) and iv ketorolac tromethamine (30 mg, single dose) in 2 cohorts eachof 8 women at caesarean delivery and to compare these findings with postpartum to quantify intrapatient changes. We documented a higher median paracetamol clearance at delivery when compared to 10–15 weeks postpartum (11.7 to 6.4 L/h·m2, P < 0.01), even after correction for weight-related changes. Similar conclusions were drawn for ketorolac: median clearance was higher at delivery with a subsequent decrease (2.03 to 1.43 L/h·m2, P < 0.05) in postpartum (17–23 weeks). These differences likely reflect pregnancy- and caesarean-delivery-related changes in drug disposition. Moreover, postpartum paracetamol clearance was significantly lower when compared to estimates published in healthy young volunteers (6.4  versus  9.6 L/h·m2), while this was not the case for ketorolac (1.43  versus  1.48 L/h·m2). This suggests that postpartum is another specific status in young women that merits focused, compound-specific pharmacokinetic evaluation

New land-use-change emissions indicate a declining CO₂ airborne fraction

About half of the anthropogenic CO2 emissions remain in the atmosphere and half are taken up by the land and ocean1. If the carbon uptake by land and ocean sinks becomes less efficient, for example, owing to warming oceans2 or thawing permafrost3, a larger fraction of anthropogenic emissions will remain in the atmosphere, accelerating climate change. Changes in the efficiency of the carbon sinks can be estimated indirectly by analysing trends in the airborne fraction, that is, the ratio between the atmospheric growth rate and anthropogenic emissions of CO2 (refs. 4–10). However, current studies yield conflicting results about trends in the airborne fraction, with emissions related to land use and land cover change (LULCC) contributing the largest source of uncertainty7,11,12. Here we construct a LULCC emissions dataset using visibility data in key deforestation zones. These visibility observations are a proxy for fire emissions13,14, which are — in turn — related to LULCC15,16. Although indirect, this provides a long-term consistent dataset of LULCC emissions, showing that tropical deforestation emissions increased substantially (0.16 Pg C decade−1) since the start of CO2 concentration measurements in 1958. So far, these emissions were thought to be relatively stable, leading to an increasing airborne fraction4,5. Our results, however, indicate that the CO2 airborne fraction has decreased by 0.014 ± 0.010 decade−1 since 1959. This suggests that the combined land–ocean sink has been able to grow at least as fast as anthropogenic emissions